Abstract:WISDoM (Wishart Distributed Matrices) is a framework for the quantification of deviation of symmetric positive-definite matrices associated with experimental samples, such as covariance or correlation matrices, from expected ones governed by the Wishart distribution. WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e.g., time series with same number of variables but different time sampling). We show t… Show more
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